Introduction

Analysis by Mitch

Analysis by Hongmei

Research Question

In this section, our analysis concentrates on examining the correlation between health expenditure as a percentage of GDP and overall life expectancy at birth to understand if higher healthcare investment translates into longer life spans. On the other hand, a specific focus also will be placed on exploring a comparative analysis of a select group of representative OECD countries, focusing on how their health expenditure influences child mortality rates under the age of 5, this comparative study aims to uncover regional differences and insights into how distinct health system structures and spending levels impact crucial health outcomes, specifically in child health.

Data Visualisations

# Plotting the relationship between health expenditure and life expectation for each year
ggplot(df, aes(x = HealthExp_percent_GDP, y = LifeExp_Total)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "blue") + # Adds a linear regression line
  labs(title = "Health Expenditure vs Life Expectation in OECD Countries",
       x = "Health Expenditure as % of GDP", 
       y = "Life Expectation at Birth") +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'
Health Expenditure vs Life Expectation at Birth in OECD Countries

Figure 1: Health Expenditure vs Life Expectation at Birth in OECD Countries 

representative_countries <- c("USA", "CAN", "GBR", "DEU", "SWE",
                              "NZL", "MEX", "JPN", "KOR", "AUS")

# Filter the dataset
filtered_data <- df %>%
                 filter(Country_Code %in% representative_countries)
# Plot the relationship between health expenditure and mortality rate in representative countries
p <- ggplot(filtered_data, aes(x = HealthExp_percent_GDP, y = Mortality_U5_Total, text = Country_Name)) +
     geom_line(aes(color = Country_Name)) +
     labs(title = "Health Expenditure vs Mortality Rate in representative countries of OECD",
          x = "Health Expenditure",
          y = "Mortality Rate Under 5") +
     theme_minimal()
interactive_plot <- ggplotly(p, tooltip = c("text", "x", "y"))
interactive_plot

Figure 2: Health Expenditure vs Mortality Rate in representative countries of OECD 

# Calculate summary statistics
summary_stats <- filtered_data %>%
  group_by(Country_Name) %>%
  summarise(across(c(HealthExp_percent_GDP, Mortality_U5_Total, LifeExp_Total),
                   list(mean = ~ mean(., na.rm = TRUE), var = ~ var(., na.rm = TRUE))))
# Make column name shorter and meaningful
colnames(summary_stats) <- c("Country", "Mean_HealthExp", "Var_HealthExp", 
                             "Mean_MortU5", "Var_MortU5", "Mean_LifeExp", "Var_LifeExp")

# Display the table
kable(summary_stats, caption = "Summary Statistics of Health Expenditure and Health Indicators in Selected Countries", format = "html") %>%
  kable_styling(full_width = TRUE)
Table 1: Summary Statistics of Health Expenditure and Health Indicators in Selected Countries
Country Mean_HealthExp Var_HealthExp Mean_MortU5 Var_MortU5 Mean_LifeExp Var_LifeExp
Australia 8.835345 1.0433705 4.828571 0.8391429 81.52056 1.2516815
Canada 10.077522 1.2059462 5.719048 0.1476190 80.95753 0.9415795
Germany 10.811329 0.5179457 4.342857 0.2835714 79.86249 1.2120146
Japan 9.186331 2.4167287 3.280952 0.3966190 82.89201 1.0666114
Korea, Rep.  5.841171 1.7191164 4.614286 2.0182857 80.00546 5.5113420
Mexico 5.585032 0.1675555 19.800000 19.3180000 74.07862 0.8985976
New Zealand 8.863087 0.6149196 6.095238 0.5914762 80.64070 1.3572151
Sweden 9.427667 2.0443653 3.219048 0.2436190 81.36376 1.0511929
United Kingdom 9.208468 1.2574710 5.266667 0.6413333 79.97224 1.5290854
United States 15.575834 2.0455827 7.361905 0.4604762 78.00999 0.5841826 
# Correlation between Health Expenditure, Mortality under 5 and Life Expectancy at birth
correlation_stats <- filtered_data %>%
    group_by(Country_Name) %>%
    summarise(
      Cor_Heal_Mort = cor(HealthExp_percent_GDP, Mortality_U5_Total, use = "complete.obs"),
      Cor_Heal_Life = cor(HealthExp_percent_GDP, LifeExp_Total, use = "complete.obs")
      )

kable(correlation_stats, caption = "Correlation between Health Expenditure, Mortality under 5 and Life Expectancy", format = "html") %>%
  kable_styling(full_width = TRUE)
Table 2: Correlation between Health Expenditure, Mortality under 5 and Life Expectancy
Country_Name Cor_Heal_Mort Cor_Heal_Life
Australia -0.9272202 0.8888673
Canada -0.9059984 0.8609486
Germany -0.8533175 0.8124925
Japan -0.9345406 0.9074473
Korea, Rep.  -0.9221641 0.9741415
Mexico -0.4719754 -0.1723541
New Zealand -0.7808487 0.8992428
Sweden -0.8681373 0.9005585
United Kingdom -0.8626432 0.8163209
United States -0.9287438 0.6117293


-Figure 1 illustrates a discernible positive relationship between health expenditure as a percentage of GDP and life expectancy at birth in OECD countries. This trend implies a potential association between greater health care investment and improved health outcomes, as evidenced by increased life expectancy.

-Figure 2 presents data from ten representative OECD countries, carefully selected to encompass a variety of healthcare systems, including universal healthcare, insurance-based, and mixed systems. The selection also includes countries that have undergone significant health reforms, such as Mexico and South Korea, and those with notable demographic characteristics, like Japan’s large population and aging society. This figure illustrates the relationship between health expenditure and infant mortality rates in these countries. A general trend is observed where higher health expenditure correlates with lower infant mortality rates, indicating a significant negative correlation and highlighting the impact of financial investment on health outcomes. Notably, the USA stands out with its higher level of health expenditure compared to other countries. However, Mexico deviates from this general trend, exhibiting a fluctuating relationship and a higher infant mortality rate than might be expected given its health expenditure level. This anomaly suggests that factors other than expenditure, such as efficiency of healthcare delivery or socio-economic conditions, may also play a critical role in determining health outcomes.

-Table 1 summarizes the mean and variance of health expenditure and key health indicators, offering a clear quantitative perspective. For instance, Canada demonstrates a high mean value for health expenditure with lower variance, indicating a consistent approach to healthcare spending. Conversely, countries like Mexico and Korea exhibit higher variance in health expenditure, warranting further investigation into the underlying causes of these fluctuations.

-Table 2 reveals a general trend where health expenditure in most countries has a strong negative correlation with the mortality rate under 5 years and a positive correlation with life expectancy. However, there are notable exceptions. The United States, for example, despite its high health expenditure, shows a relatively lower positive correlation (0.6117293) with life expectancy. This suggests that increased spending does not correspond with a proportionate increase in life expectancy, possibly reflecting inefficiencies or other influencing factors. Mexico presents a distinct case, displaying a much weaker negative correlation with life expectancy (-0.1723541) and a less pronounced relationship between health expenditure and the reduction of mortality under 5 years. These anomalies in the Mexican context point towards potential systemic issues in healthcare delivery or socio-economic factors impacting health outcomes.

Conclusion

References

Phebe A Owusu, Samuel A Sarkodie(2021).Relationship between mortality and health care expenditure:Sustainable assessment of healthcare system. https://doi.org/10.1371/journal.pone.0247413.